public final class NGramTokenFilter extends TokenFilter
If you were using this TokenFilter
to perform partial highlighting,
this won't work anymore since this filter doesn't update offsets. You should
modify your analysis chain to use NGramTokenizer
, and potentially
override NGramTokenizer.isTokenChar(int)
to perform pre-tokenization.
AttributeSource.State
Modifier and Type | Field and Description |
---|---|
static int |
DEFAULT_MAX_NGRAM_SIZE
Deprecated.
since 7.4 - this value will be required.
|
static int |
DEFAULT_MIN_NGRAM_SIZE
Deprecated.
since 7.4 - this value will be required.
|
static boolean |
DEFAULT_PRESERVE_ORIGINAL |
input
DEFAULT_TOKEN_ATTRIBUTE_FACTORY
Constructor and Description |
---|
NGramTokenFilter(TokenStream input)
Deprecated.
since 7.4. Use
NGramTokenFilter(TokenStream, int, int, boolean) instead. |
NGramTokenFilter(TokenStream input,
int gramSize)
Creates an NGramTokenFilter that produces n-grams of the indicated size.
|
NGramTokenFilter(TokenStream input,
int minGram,
int maxGram)
Deprecated.
since 7.4. Use
NGramTokenFilter(TokenStream, int, int, boolean) instead. |
NGramTokenFilter(TokenStream input,
int minGram,
int maxGram,
boolean preserveOriginal)
Creates an NGramTokenFilter that, for a given input term, produces all
contained n-grams with lengths >= minGram and <= maxGram.
|
Modifier and Type | Method and Description |
---|---|
void |
end()
This method is called by the consumer after the last token has been
consumed, after
TokenStream.incrementToken() returned false
(using the new TokenStream API). |
boolean |
incrementToken()
Consumers (i.e.,
IndexWriter ) use this method to advance the stream to
the next token. |
void |
reset()
This method is called by a consumer before it begins consumption using
TokenStream.incrementToken() . |
close
addAttribute, addAttributeImpl, captureState, clearAttributes, cloneAttributes, copyTo, endAttributes, equals, getAttribute, getAttributeClassesIterator, getAttributeFactory, getAttributeImplsIterator, hasAttribute, hasAttributes, hashCode, reflectAsString, reflectWith, removeAllAttributes, restoreState, toString
@Deprecated public static final int DEFAULT_MIN_NGRAM_SIZE
@Deprecated public static final int DEFAULT_MAX_NGRAM_SIZE
public static final boolean DEFAULT_PRESERVE_ORIGINAL
public NGramTokenFilter(TokenStream input, int minGram, int maxGram, boolean preserveOriginal)
input
- TokenStream
holding the input to be tokenizedminGram
- the minimum length of the generated n-gramsmaxGram
- the maximum length of the generated n-gramspreserveOriginal
- Whether or not to keep the original term when it
is shorter than minGram or longer than maxGrampublic NGramTokenFilter(TokenStream input, int gramSize)
input
- TokenStream
holding the input to be tokenizedgramSize
- the size of n-grams to generate.@Deprecated public NGramTokenFilter(TokenStream input, int minGram, int maxGram)
NGramTokenFilter(TokenStream, int, int, boolean)
instead.
Behaves the same as
NGramTokenFilter(input, minGram, maxGram, false)
input
- TokenStream
holding the input to be tokenizedminGram
- the minimum length of the generated n-gramsmaxGram
- the maximum length of the generated n-grams@Deprecated public NGramTokenFilter(TokenStream input)
NGramTokenFilter(TokenStream, int, int, boolean)
instead.
Behaves the same as
NGramTokenFilter(input, 1, 2, false)
input
- TokenStream
holding the input to be tokenizedpublic final boolean incrementToken() throws java.io.IOException
TokenStream
IndexWriter
) use this method to advance the stream to
the next token. Implementing classes must implement this method and update
the appropriate AttributeImpl
s with the attributes of the next
token.
The producer must make no assumptions about the attributes after the method
has been returned: the caller may arbitrarily change it. If the producer
needs to preserve the state for subsequent calls, it can use
AttributeSource.captureState()
to create a copy of the current attribute state.
This method is called for every token of a document, so an efficient
implementation is crucial for good performance. To avoid calls to
AttributeSource.addAttribute(Class)
and AttributeSource.getAttribute(Class)
,
references to all AttributeImpl
s that this stream uses should be
retrieved during instantiation.
To ensure that filters and consumers know which attributes are available,
the attributes must be added during instantiation. Filters and consumers
are not required to check for availability of attributes in
TokenStream.incrementToken()
.
incrementToken
in class TokenStream
java.io.IOException
public void reset() throws java.io.IOException
TokenFilter
TokenStream.incrementToken()
.
Resets this stream to a clean state. Stateful implementations must implement this method so that they can be reused, just as if they had been created fresh.
If you override this method, always call super.reset()
, otherwise
some internal state will not be correctly reset (e.g., Tokenizer
will
throw IllegalStateException
on further usage).
NOTE:
The default implementation chains the call to the input TokenStream, so
be sure to call super.reset()
when overriding this method.
reset
in class TokenFilter
java.io.IOException
public void end() throws java.io.IOException
TokenFilter
TokenStream.incrementToken()
returned false
(using the new TokenStream
API). Streams implementing the old API
should upgrade to use this feature.
This method can be used to perform any end-of-stream operations, such as setting the final offset of a stream. The final offset of a stream might differ from the offset of the last token eg in case one or more whitespaces followed after the last token, but a WhitespaceTokenizer was used.
Additionally any skipped positions (such as those removed by a stopfilter) can be applied to the position increment, or any adjustment of other attributes where the end-of-stream value may be important.
If you override this method, always call super.end()
.
NOTE:
The default implementation chains the call to the input TokenStream, so
be sure to call super.end()
first when overriding this method.
end
in class TokenFilter
java.io.IOException
- If an I/O error occursCopyright © 2000–2019 The Apache Software Foundation. All rights reserved.